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Gap-filling Fast Electrical Mobility Spectrometer Measurements of Particle Number Size Distributions for Eddy Covariance Application

Category: Urban Air Quality

Accepted Manuscripts
DOI: 10.4209/aaqr.2019.06.0291
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Agnes Meyer-Kornblum , Lars Gerling, Stephan Weber

  • Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, 38106 Braunschweig, Germany


  • Eddy covariance shall be used to measure size-resolved particle number fluxes.
  • Fast measurements (≥ 10 Hz) of particle number size distributions are needed.
  • Size-channel concentrations can fall below the minimum threshold of analyser.
  • Missing concentrations are calculated by three different gap-filling methods.
  • Three different gap-filling methods are evaluated for data set from Berlin, Germany.


To estimate the spatial and temporal variation of urban particle number concentrations (PNC), e.g. for exposure studies, a better knowledge of the exchange of particles between the urban surface and the atmosphere is important. Size-resolved fluxes of PNCs were quantified in Berlin, Germany, using the micrometeorological eddy covariance technique. The method requires fast measurements of particle number size distributions (PNSD) by a fast particle spectrometer. The Engine Exhaust Particle Sizer Spectrometer (EEPS, TSI Inc., Model 3090) is designed for fast (10 Hz), high concentration measurements in the size range 5.6 nm < Dp < 560 nm, e.g. in the exhaust plume of engines. In the urban background environment of Berlin, however, PNCs in some size channels can temporarily fall below the minimum threshold concentration of the analyser. Hence, missing concentrations in respective size channels lead to gaps in the PNSD. In the present study, three gap-filling methods were applied to derive complete PNSDs: linear interpolation (LI), natural spline interpolation (NSI) and log-normal fitting (LNF). For method evaluation, 105 PNSDs without gaps were used in which different numbers of artificial gaps were implemented. Using three different data sets the comparison illustrates that LI and NSI (LI: R² = 0.84 to 0.94, NSI: R² = 0.84 to 0.95) outperform LNF (R² = 0.78 to 0.88). With regard to the Berlin data set, NSI is the recommended gap-filling method since it results in a lower average uncertainty of between 10.5% and 21.8% rather than 13.3% and 22.9% for LI. It is advisable to reject the boundary areas of the PNSD from gap-filling, i.e. Dp < 10 nm and Dp > 200 nm, as this considerably improves the gap-filling quality. For applications in which gaps occur in PNSD data sets using the EEPS, this study gives advice on how to gap-fill data sets.


Micrometeorology Engine Exhaust Particle Sizer Spectrometer Berlin Particle fluxes Ultrafine

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